1. Waikato Environment for Knowledge Analysis

2. Contents What is WEKA? The Explorer: References and Resources
Preprocess data
Data Visualization
Classification
Clustering
Association Rules
Attribute Selection
References and Resources

3. What is WEKA? Waikato Environment for Knowledge Analysis
It’s a data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand.
Weka is also a bird found only on the islands of New Zealand.

4. Getting started with weka
Free : License GNU
Multi platform (Java): Windows, Mac, Linux
Easy to install
Examples of dataset
Documentation, Tutorial and Mooc

5. What is WEKA? Data Mining tool
User interface / Integrated to your Java code
Data filters
Classification
Clustering
Visualization

6. Weka

7. Input data ARFF (Attribute-Relation File Format) CSV SQL Database
Name of the dataset
Attributes’ name, value and type
Data

8. The explorer Preprocessing Visualization Classification Clustering
Finding associations
Attribute selection

9. Explorer: Preprocessing
49 different filters.
Supervised, unsupervised
On attributes or instances

10. Demo: Filters

11. Demo: Visualization

12. Explorer: Classifier 76 different classification algorithms
Decision trees, instance-based classifiers, support vector machines (SVM), Bayes’ nets…

13. Demo: Classifier

14. Contents What is WEKA? The Explorer: References and Resources
Preprocess data
Data Visualization
Classification
Clustering
Association Rules
Attribute Selection
References and Resources

15. Input data ARFF (Attribute-Relation File Format) CSV SQL Database
Name of the dataset
Attributes’ name, value and type
Data

16. Contents What is WEKA? The Explorer: References and Resources
Preprocess data
Data Visualization
Classification
Clustering
Association Rules
Attribute Selection
References and Resources

17. Explorer: clustering data
WEKA contains “clusterers” for finding groups of similar instances in a dataset
Implemented schemes are:
- k-Means, EM, Cobweb, X-means, FarthestFirst
Clusters can be visualized and compared to “true” clusters (if given)
Evaluation based on loglikelihood if clustering scheme produces a probability distribution

18. K-Means Clustering (contd.)
Example

19. The K-Means Clustering Method
Given k, the k-means algorithm is implemented in four steps:
Partition objects into k nonempty subsets
Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster)
Assign each object to the cluster with the nearest seed point
Go back to Step 2, stop when no more new assignment

20. Demo : Clustering Data

21. Explorer: Finding associations
WEKA contains an implementation of the Apriori algorithm for learning association rules
Works only with discrete data
Can identify statistical dependencies between groups of attributes:
milk, butter  bread, eggs (with confidence 0.9 and support 2000)
Apriori can compute all rules that have a given minimum support and exceed a given confidence
9/21/2018

22. Basic Concepts: Frequent Patterns
Tid
Items bought 10
Beer, Nuts, Diaper 20
Beer, Coffee, Diaper 30
Beer, Diaper, Eggs 40
Nuts, Eggs, Milk 50
Nuts, Coffee, Diaper, Eggs, Milk
itemset: A set of one or more items
k-itemset X = {x1, …, xk}
(absolute) support, or, support count of X: Frequency or occurrence of an itemset X
(relative) support, s, is the fraction of transactions that contains X (i.e., the probability that a transaction contains X)
An itemset X is frequent if X’s support is no less than a minsup threshold
Customer
buys diaper
buys both
buys beer
September 21, 2018

23. Basic Concepts: Association Rules
Tid
Items bought
Find all the rules X  Y with minimum support and confidence
support, s, probability that a transaction contains X  Y
confidence, c, conditional probability that a transaction having X also contains Y
Let minsup = 50%, minconf = 50%
Freq. Pat.: Beer:3, Nuts:3, Diaper:4, Eggs:3, {Beer, Diaper}:3 10
Beer, Nuts, Diaper 20
Beer, Coffee, Diaper 30
Beer, Diaper, Eggs 40
Nuts, Eggs, Milk 50
Nuts, Coffee, Diaper, Eggs, Milk
Customer
buys both
Customer
buys diaper
Customer
buys beer
Association rules: (many more!)
Beer  Diaper (60%, 100%)
Diaper  Beer (60%, 75%)
September 21, 2018

24. Demo : Association

25. Explorer: attribute selection
Panel that can be used to investigate which (subsets of) attributes are the most predictive ones
Attribute selection methods contain two parts:
A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking
An evaluation method: correlation-based, wrapper, information gain, chi-squared, …
Very flexible: WEKA allows (almost) arbitrary combinations of these two
9/21/2018

26. Demo : Attribute Selection

27. Different ways to use it
Explorer : Preprocessor, clustering, classifier, regression analysis, visualization
Experimenter: Analysis and comparison of classifiers

28. Different ways to use it
Simple Command Line Instructions
Can be integrated to your java code

29. Weka’s Advantages: Contains a lot of algorithms
Free (most other Data Mining tools are very expensive)
Open source, so adapting it to your own needs is possible
Constantly under development (not only by the original designers)

30. Drawbacks Lack of possibilities to interface with other software
Performance is often sacrificed in favor of portability, design transparency, etc.
Memory limitation, because the data has to be loaded into main memory completely

31. Conclusion
+ Easy to use + No programming skill needed - Visualization and statistical tools limited